基于内容的标准烟叶图像数据库检索

基于内容的检索是当前多媒体数据库发展的一个重要研究领域。在图像数据库中,基于内容的图像检索技术一般采用颜色直方图为特征。该文提出了把图像的形状特征、颜色特征和纹理特征结合起来的方法进行基于内容的图像检索。通过对人的视觉检索过程的研究,提出把数据库中的图像依次分别按形状特征、颜色特征和纹理特征分级聚类的方法,既符合人的视觉特点又大大提高了检索效率。     

An object-based image retrieval system for digital libraries

A novel approach to clustering for image segmentation and a new object-based image retrieval method are proposed. The clustering is achieved using the Fisher discriminant as an objective function. The objective function is improved by adding a spatial constraint that encourages neighboring pixels to take on the same class label. A six-dimensional feature vector is used for clustering by way of the combination of color and busyness features for each pixel. After clustering, the dominant segments in each class are chosen based on area and used to extract features for image retrieval. The color content is represented using a histogram, and Haar wavelets are used to represent the texture feature of each segment. The image retrieval is segment-based; the user can select a query segment to perform the retrieval and assign weights to the image features. The distance between two images is calculated using the distance between features of the constituent segments. Each image is ranked based on this distance with respect to the query image segment. The algorithm is applied to a pilot database of natural images and is shown to improve upon the conventional classification and retrieval methods. The proposed segmentation leads to a higher number of relevant images retrieved, 83.5% on average compared to 72.8 and 68.7% for the k-means clustering and the global retrieval methods, respectively.     

SemQuery: semantic clustering and querying on heterogeneous features for visual data

The effectiveness of content-based image retrieval can be enhanced using heterogeneous features embedded in the images. However, since the features in texture, color, and shape are generated using different computation methods and thus may require different similarity measurements, the integration of the retrievals on heterogeneous features is a nontrivial task. We present a semantics-based clustering and indexing approach, termed SemQuery, to support visual queries on heterogeneous features of images. Using this approach, the database images are classified based on their heterogeneous features. Each semantic image cluster contains a set of subclusters that are represented by the heterogeneous features that the images contain. An image is included in a semantic cluster if it falls within the scope of all the heterogeneous clusters of the semantic cluster. We also design a neural network model to merge the results of basic queries on individual features. A query processing strategy is then presented to support visual queries on heterogeneous features. An experimental analysis is conducted and presented to demonstrate the effectiveness and efficiency of the proposed approach.     

基于小波模糊聚类的均质纹理和非均质纹理图象检索

基于内容的图象检索是近年来的研究热点 ,为此提出了一种自动区分均质纹理和非均质纹理图象 ,并对这两类图象分别进行检索的算法 .算法首先从图象离散小波变换的低频子带提取一定的颜色和纹理特征用于模糊聚类 ,将图象的低频子带分割为一定的区域 ;然后根据分割的结果将图象自动语义分类为均质纹理或者非均质纹理图象 ;最后对均质纹理和非均质纹理图象分别提取不同的特征矢量 ,并按照一定的相似度准则检索图象 .实验结果表明 ,该算法具有良好的均质纹理和非均质纹理图象分类和检索性能 .     

On size invariance texture image retrieval by fuzzy logic classifier and scattering statistical features

The texture image retrieval plays an important role in everyday life of people. In this paper, a new and efficient image features extraction approach based on scattering transform is proposed for size invariance texture image retrieval. The proposed approach obtains texture information in different directions and scales. And, analysis of size invariance texture image retrieval using fuzzy logic classifier and scattering statistical features is carried out. The different size samples of texture image are randomly generated from the original texture images. Also, average success rate of each size samples is obtained, respectively. The study shows that statistical features can achieve good performance from the sixth feature.     

低层特征获取高层语义的图像检索

首先采用基于颜色聚类的方法将图像分割成区域,提取每个区域的Gabor小波纹理特征和灰度共生矩阵纹理特征,接着采用信息熵对特征进行选择,使用选择后的特征对图像区域进行聚类,得到每幅图像的语义特征向量;然后提出遗传模糊C均值算法对图像进行聚类。在图像检索时,查询图像和聚类中心比较,在距离最小的类中进行检索。实验表明,提出的方法可以明显提高检索效率,提高了检索的精度。     

一种基于颜色统计聚类的医学图像检索技术

基于颜色检索的基本思想是将图像间的距离归结为其颜色直方图间的相似性度量,从而图像检索也就转化为颜色直方图的匹配。目前基于颜色检索的算法主要集中在不同颜色空间进行全局颜色聚类或融合其他可视特征(纹理,颜色空间信息等)联合检索两个方向上。该文在具体的结肠镜图像检索系统研究中,根据医学图像的特点,提出一种在HSV空间的颜色统计聚类的检索方法,取得了良好的检索效果。     

一种基于混合特征核的图像检索方法

为了更准确地描述图像的视觉特征,提高图像检索的查准率与查全率,提出了一种基于混合特征核的图像检索方法.该方法提取图像的颜色、纹理、SIFT特征,引入高斯核函数,建立图像的混合特征核模型,在高维的核空间进行基于核的图像聚类.实验表明,该混合模型与传统多特征融合方法以及单一特征核方法相比,能够更好地表示图像的视觉特征,提高检索的查准率和查全率.     

Multiple representations, similarity matching, and results fusion for content-based image retrieval

In this paper, we show how the use of multiple content representations and their fusion can improve the performance of content-based image retrieval systems. We consider the case of texture and propose a new algorithm for texture retrieval based on multiple representations and their results fusion. Texture content is modeled using two different models: the well-known autoregressive model and a perceptual model based on perceptual features such as coarseness and directionality. In the case of the perceptual model, two viewpoints are considered: perceptual features are computed based on the original images viewpoint and on the autocovariance function viewpoint (corresponding to original images). So we consider a total of three content representations. The similarity measure used is based on Gower's index of similarity. Simple results of the fusion models are used to merge search results returned by different representations. Experimentations and benchmarking carried out on the well-known Brodatz database show a drastic improvement in search effectiveness with the fused model without necessarily altering their efficiency in an important way.     

基于评价指标组合特征的三维模型检索方法

特征组合是提高三维模型检索有效性的一种重要手段,为了能更有效地引导特征组合,提出一种借助检索有效性单值评价指标来进行特征组合的方法.该方法采用了深度图、视图特征集、法向量信息熵和射线4种特征,首先对训练集分别计算这4种特征的检索有效性单值评价指标,并依据这些评价指标来确定特征距离的权重;然后在对测试集的检索中,使用权重来组合根据单一特征得到的特征距离,以度量三维模型的相似性.实验结果表明,文中方法的检索有效性优于经典的DESIRE特征组合方法.     

A retrieval system to analyse dermatological lesions using feature ortho-normalisation

An information retrieval system is proposed as an assistance tool for diagnosing the skin lesion using Content-Based Image Retrieval approach. Efficiency of the retrieval system is deliberated in terms of the most relevant retrieval of images from database. The proposed diagnostic assistive model retrieves the skin lesion images and its disease category, case history, symptoms and treatment plan. This retrieval process is made from a dermatology database by the way of visual features in the input image such as shape, texture and colour. The author’s proposed principal component analysis (PCA) feature projection technique is to discriminate the features by projecting them onto a feature subspace. While projecting the features onto a feature subspace features are normalised orthogonally. So the proposed methodology is used to improve the classification by the way of discriminate the features, in-turn it focus the retrieval of comprehensive reference sources, so that the diagnosis accuracy of the dermatologists are also improved. Receiver-operating characteristic curve is used to analyse the proposed computer-aided diagnosis (CAD) method, while analysis we attained high contribution to detect the skin lesions. Totally 1450 images are experimented and the system produced the 99.09% specificity, 96.69% sensitivity and 98.3% accuracy. When compared with other works this system of assessment shows high retrieval and diagnosis concert.     

基于纹理和高斯密度特征的图像检索算法

直接从DCT域中提取图像的特征是提高图像的检索效率的方法.直接从压缩域中提取图像的高斯密度,即计算图像在8个方向上的分段累加值,形成一个8*4的二维向量,再结合图像的纹理特征来进行图像检索.为了验证算法的可行性,建立了10000幅图像的图像库.实验结果表明,该方法能够准确地检索出目标图像,有效地提高了图像检索的精度和速度.     

Unsupervised feature selection applied to content-based retrieval of lung images

This paper describes a new hierarchical approach to content-based image retrieval called the "customized-queries" approach (CQA). Contrary to the single feature vector approach which tries to classify the query and retrieve similar images in one step, CQA uses multiple feature sets and a two-step approach to retrieval. The first step classifies the query according to the class labels of the images using the features that best discriminate the classes. The second step then retrieves the most similar images within the predicted class using the features customized to distinguish "subclasses" within that class. Needing to find the customized feature subset for each class led us to investigate feature selection for unsupervised learning. As a result, we developed a new algorithm called FSSEM (feature subset selection using expectation-maximization clustering). We applied our approach to a database of high resolution computed tomography lung images and show that CQA radically improves the retrieval precision over the single feature vector approach. To determine whether our CBIR system is helpful to physicians, we conducted an evaluation trial with eight radiologists. The results show that our system using CQA retrieval doubled the doctors' diagnostic accuracy.     

Feed-forward content based image retrieval using adaptive tetrolet transforms

This paper proposes a new approach for content based image retrieval based on feed-forward architecture and Tetrolet transforms. The proposed method addresses the problems of accuracy and retrieval time of the retrieval system. The proposed retrieval system works in two phases: feature extraction and retrieval. The feature extraction phase extracts the texture, edge and color features in a sequence. The texture features are extracted using Tetrolet transform. This transform provides better texture analysis by considering the local geometry of the image. Edge orientation histogram is used for retrieving the edge feature while color histogram is used for extracting the color features. Further retrieval phase retrieves the images in the feed-forward manner. At each stage, the number of images for next stage is reduced by filtering out irrelevant images. The Euclidean distance is used to measure the distance between the query and database images at each stage. The experimental results on COREL- 1 K and CIFAR - 10 benchmark databases show that the proposed system performs better in terms of the accuracy and retrieval time in comparison to the state-of-the-art methods.     

基于互补特征的纹理图像检索

针对互补特征可以有效地改善图像检索系统性能的特点,提出一种在改进Contourlet变换域采用L1能量与广义高斯分布参数特征的纹理图像检索方法。首先,应用改进的方法对方向子带系数进行广义高斯统计建模。然后,分别单独利用各个特征和相应的相似性测度进行检索。最后,基于直接的相似性测度和,采用这两种互补的特征进行检索。实验结果表明,和采用单一特征相比较,互补特征由于充分地反映了图像的结构信息和随机分布信息,从而有效地提高了纹理图像数据库的平均检索率。     

HIRBIR: A hierarchical approach to region-based image retrieval

This paper proposes a hierarchical approach to region-based image retrieval (HIRBIR) based on wavelet transform whose decomposition property is similar to human visual processing. First, automated image segmentation is performed fast in the low-low (LL) frequency subband of the wavelet domain that shows the desirable low image resolution. In the proposed system, boundaries between segmented regions are deleted to improve the robustness of region-based image retrieval against segmentation-related uncertainty. Second, a region feature vector is hierarchically represented by information in all wavelet subbands, and each feature component of a feature vector is a unified color–texture feature. Such a feature vector captures well the distinctive features (e.g., semantic texture) inside one region. Finally, employing a hierarchical feature vector, the weighted distance function for region matching is tuned meaningfully and easily, and a progressive stepwise indexing mechanism with relevance feedback is performed naturally and effectively in our system. Through experimental results and comparison with other methods, the proposed HIRBIR shows a good tradeoff between retrieval effectiveness and efficiency as well as easy implementation for region-based image retrieval.     

Vector space model adaptation and pseudo relevance feedback for content-based image retrieval

Image retrieval is an important problem for researchers in computer vision and content-based image retrieval (CBIR) fields. Over the last decades, many image retrieval systems were based on image representation as a set of extracted low-level features such as color, texture and shape. Then, systems calculate similarity metrics between features in order to find similar images to a query image. The disadvantage of this approach is that images visually and semantically different may be similar in the low level feature space. So, it is necessary to develop tools to optimize retrieval of information. Integration of vector space models is one solution to improve the performance of image retrieval. In this paper, we present an efficient and effective retrieval framework which includes a vectorization technique combined with a pseudo relevance model. The idea is to transform any similarity matching model (between images) to a vector space model providing a score. A study on several methodologies to obtain the vectorization is presented. Some experiments have been undertaken on Wang, Oxford5k and Inria Holidays datasets to show the performance of our proposed framework.     

综合纹理特征和语义信息的医学图像检索

提出一种结合图像分块纹理特征和语义信息的医学胸片图像检索方法。同时,介绍了颜色特征提取方法中的颜色相关图算法。据此,实现了一个图像检索原型系统,依据所设计的评价实验,将不同实验的检索结果进行了比较和分析。实验证明,结合图像分块纹理特征和语义信息的检索方法具有较好的检索效果。